Comparison of energy requirement estimation using activity record or accelerometer with doubly labeled water method in collegiate male sprinters.

BACKGROUND & AIMS: Track and field sprinters must obtain an optimal body composition to improve sprint performance. To successfully change body composition, it is important to evaluate the estimated energy requirements (EER) and fluctuations in total energy expenditure (TEE). However, methods to accurately evaluate the EER and TEE in sprinters have not been fully investigated. The aim of this study was to compare currently used methods with the doubly labeled water (DLW) method, which is currently the gold standard for evaluating EER and TEE. METHODS: Ten male collegiate sprinters participated in the study. We evaluated TEEDLW and compared it with the EER calculated using two equations used by the National Institute of Health and Nutrition (NIHN) and the Japan Institute of Sports Sciences (JISS). In addition, we evaluated the TEE from the activity record (AR) and triaxial accelerometer (ACC). RESULTS: TEEDLW (3172 ± 415 kcal/day) was not significantly different from EERNIHN (p = 0.076) or EERJISS (p = 0.967). In addition, there were no significant differences between TEEDLW and TEEAR (p = 0.218). However, two accelerometer-derived equations used to evaluate TEE were found to have underestimated (2783 ± 377 kcal/day, p < 0.001) and overestimated (3405 ± 369 kcal/day, p = 0.009) the TEE. CONCLUSION: Our results suggest that EERNIHN and EERJISS may be useful in evaluating the EER of collegiate male sprinters on a group basis, and AR may be more accurate than ACC in evaluating the TEE. These results may be helpful when considering nutritional support for male collegiate sprinters.

Effects of Childcare Hands-On Gardening on Preschoolers' (3-5 Years) Physical Activity in Semi-Arid Climate Zone.

How hands-on gardening impacts behaviors including healthy eating and physical activity during early childhood can be of critical importance for preventing the early onset of obesity. This study investigates how participating in hands-on gardening impacts preschoolers' (3-5 years old) physical activity (measured by accelerometers) in childcare centers in the semi-arid climate zone. The research was conducted in eight licensed childcare centers located in West Texas with 149 children (n = 149). Four childcare centers in the experimental group received hands-on garden interventions; the other four in the control group did not. In both experimental (intervention) and control (non-intervention) centers, children wore Actigraph GT3X+ accelerometers continuously for 5 days before and for 5 days after intervention (a total of 10 days). Results show that the duration of sedentary behavior of children in the experimental (intervention) group significantly decreased compared to children in the control (non-intervention) group. The finding suggests that the positive effects of childcare hands-on gardening on physical activity extend to semi-arid climate zones where gardening is challenging due to high temperatures and lack of annual rainfall. The research emphasizes the critical need to incorporate hands-on gardening in childcare centers as an obesity prevention strategy nationally in the US and beyond.

Investigating young children's physical activity through time and place.

BACKGROUND: Previous research indicates the start of primary school (4-5-year-old) as an essential period for the development of children's physical activity (PA) patterns, as from this point, the age-related decline of PA is most often observed. During this period, young children are exposed to a wider variety of environmental- and social contexts and therefore their PA is influenced by more diverse factors. However, in order to understand children's daily PA patterns and identify relevant opportunities for PA promotion, it is important to further unravel in which (social) contexts throughout the day, PA of young children takes place. METHODS: We included a cross-national sample of 21 primary schools from the Startvaardig study. In total, 248 children provided valid accelerometer and global positioning (GPS) data. Geospatial analyses were conducted to quantify PA in (social) environments based on their school and home. Transport-related PA was evaluated using GPS speed-algorithms. PA was analysed at different environments, time-periods and for week- and weekend days separately. RESULTS: Children accumulated an average of 60 min of moderate-to-vigorous PA (MVPA), both during week- and weekend days. Schools contributed to approximately half of daily MVPA during weekdays. During weekends, environments within 100 m from home were important, as well as locations outside the home-school neighbourhood. Pedestrian trips contributed to almost half of the daily MVPA. CONCLUSIONS: We identified several social contexts relevant for children's daily MVPA. Schools have the potential to significantly contribute to young children's PA patterns and are therefore encouraged to systematically evaluate and implement parts of the school-system that stimulate PA and potentially also learning processes. Pedestrian trips also have substantial contribution to daily MVPA of young children, which highlights the importance of daily active transport in school- and parental routines.

Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data.

BACKGROUND: Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD). METHOD: We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo. RESULTS: Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group. CONCLUSIONS: HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.

Replacing sedentary time for physical activity: Does intensity matter for body composition in oldest-old adults?

To assess the independent and combined relationships among objectively measured sedentary time (ST), light intensity PA (LPA), and moderate-to-vigorous intensity PA (MVPA) with muscle mass and fat mass (FM) and how theoretical displacement of these inter-dependent behaviours relates to body composition in oldest-old men. A total of 1046 men participating in the year 14 visit of the prospective Osteoporotic Fractures in Men (MrOS) cohort study with complete data for accelerometry, dual x-ray absorptiometry, and deuterated creatine dilution (D3Cr) muscle mass were included in the analysis (84.0 ± 3.8 yrs.). Single, partition, and isotemporal substitution models were used to assess the interrelationships between PA intensities and ST with body composition measures, while controlling for relevant confounders. Replacing 30-min of ST with 30-min of MVPA was associated with lower FM (ß =-0.17, p < 0.001) and higher D3Cr muscle mass, although this was of borderline significance (ß = 0.07, p = 0.05). Replacing 30-min of ST for LPA was associated with lower FM (ß =-0.15, p < 0.001), but there was no effect on D3Cr muscle mass (p > 0.05). Exchanging ST with any intensity of PA is associated with benefits for FM in oldest-old adult men, although substitution with MVPA may be more beneficial than LPA for maintaining/improving skeletal muscle mass.

Sports participation in childhood and adolescence and physical activity intensity in adulthood.

OBJECTIVES: The aim of the present study was to analyze the association between sports participation in childhood and adolescence and the practice of physical activity at different intensities in adulthood, and to verify if some sports participation characteristics such as number of sports; type of sport (individual, collective or a combination of both) and total estimated sports participation time are associated with the different physical activity intensities in adulthood. DESIGN: This is a cross-sectional study. METHODS: This study included 129 young adults of both sexes aged 18-25 years. Sports participation in childhood (7-10 years) and adolescence (11-17 years) was retrospectively estimated through specific questionnaire. Light, moderate, vigorous and moderate to vigorous intensity physical activity was objectively estimated by accelerometers. To verify the association between SP in childhood and adolescence and BP intensities in adults, multiple linear regression was adopted, with 5% significance. RESULTS: Analyses showed that, in females, sports participation in childhood (ß = 0.315; R2 = 0.14; P = 0.020) and persistence in sports participation (ß = 0.364; R2 = 0.18; P = 0.007) were positive predictors of vigorous physical activity in adulthood. In addition, the comparison according to the specificities of the sport practice, indicated that participation in two or more sports in childhood, one sport and collective sports in adolescence and at least one year of sports participation throughout childhood and adolescence were associated with longer time in vigorous physical activity intensity and MVPA (minutes/day) in adult females (P < 0.05). CONCLUSIONS: It could be concluded that sports participation indicators in childhood and adolescence were considered predictors of vigorous physical activity in adult females. In addition, number of sports, type of sport and practice time in childhood and adolescence seem to predict vigorous and moderate to vigorous levels of physical activity for adult females.

Self-reported and accelerometry measures of sleep components in adolescents living in Pacific Island countries and territories: Exploring the role of sociocultural background.

OBJECTIVES: The objective of this study is to assess the concordance and its association with sociocultural background of a four-question survey with accelerometry in a multiethnic adolescent population, regarding sleep components. Based on questions from the Pittsburgh Sleep Quality Index and adapted to a school context, the questionnaire focussed on estimating sleep onset time, wake-up time and sleep duration on both weekdays and weekends. This subjective survey was compared with accelerometry data while also considering the influence of sociocultural factors (sex, place of living, ethnic community and socio-economic status). METHODS: Adolescents aged 10.5-16 years (n = 182) in New Caledonia completed the survey and wore an accelerometer for seven consecutive days. Accelerometry was used to determine sleep onset and wake-up time using validated algorithms. Based on response comparison, Bland-Altman plots provided agreement between subjective answers and objective measures. We categorized participants' answers to the survey into underestimated, aligned and overestimated categories based on time discrepancies with accelerometry data. Multinomial regressions highlighted the sociocultural factors associated with discrepancies. RESULTS: Concordance between the accelerometer and self-reported assessments was low particularly during weekends (18%, 26% and 19% aligned for onset sleep time, wake-up time and sleep duration respectively) compared with weekdays (36%, 53% and 31% aligned, respectively). This means that the overall concordance was less than 30%. When considering the sociocultural factors, only place of living was associated with discrepancies in onset sleep time and wake-up time primarily on weekdays. Rural adolescents were more likely to overestimate both onset sleep time (B = -1.97, p < 0.001) and wake-up time (B = -1.69, p = 0.003). CONCLUSIONS: The study found low concordance between self-assessment and accelerometry outputs for sleep components. This was particularly low for weekend days and for participants living in rural areas. While the adapted four-item questionnaire was useful and easy to complete, caution should be taken when making conclusions about sleep habits based solely on this measurement.

Validation of physical activity levels from shank-placed Axivity AX6 accelerometers in older adults.

This cross-sectional study aimed to identify and validate cut-points for measuring physical activity using Axivity AX6 accelerometers positioned at the shank in older adults. Free-living physical activity was assessed in 35 adults aged 55 and older, where each participant wore a shank-mounted Axivity and a waist-mounted ActiGraph simultaneously for 72 hours. Optimized cut-points for each participant's Axivity data were determined using an optimization algorithm to align with ActiGraph results. To assess the validity between the physical activity assessments from the optimized Axivity cut-points, a leave-one-out cross-validation was conducted. Bland-Altman plots with 95% limits of agreement, intraclass correlation coefficients (ICC), and mean differences were used for comparing the systems. The results indicated good agreement between the two accelerometers when classifying sedentary behaviour (ICC = 0.85) and light physical activity (ICC = 0.80), and moderate agreement when classifying moderate physical activity (ICC = 0.67) and vigorous physical activity (ICC = 0.70). Upon removal of a significant outlier, the agreement was slightly improved for sedentary behaviour (ICC = 0.86) and light physical activity (ICC = 0.82), but substantially improved for moderate physical activity (ICC = 0.81) and vigorous physical activity (ICC = 0.96). Overall, the study successfully demonstrated the capability of the resultant cut-point model to accurately classify physical activity using Axivity AX6 sensors placed at the shank.

Predictors associated with an increase in daily steps among people with prediabetes or type 2 diabetes participating in a two-year pedometer intervention.

BACKGROUND: This study aimed to explore predictors associated with intermediate (six months) and post-intervention (24 months) increases in daily steps among people with prediabetes or type 2 diabetes participating in a two-year pedometer intervention. METHODS: A secondary analysis was conducted based on data from people with prediabetes or type 2 diabetes from two intervention arms of the randomised controlled trial Sophia Step Study. Daily steps were measured with an ActiGraph GT1M accelerometer. Participants were divided into two groups based on their response to the intervention: Group 1) ≥ 500 increase in daily steps or Group 2) a decrease or < 500 increase in daily steps. Data from baseline and from six- and 24-month follow-ups were used for analysis. The response groups were used as outcomes in a multiple logistic regression together with baseline predictors including self-efficacy, social support, health-related variables, intervention group, demographics and steps at baseline. Predictors were included in the regression if they had a p-value < 0.2 from bivariate analyses. RESULTS: In total, 83 participants were included. The mean ± SD age was 65.2 ± 6.8 years and 33% were female. At six months, a lower number of steps at baseline was a significant predictor for increasing ≥ 500 steps per day (OR = 0.82, 95% CI 0.69-0.98). At 24 months, women had 79% lower odds of increasing ≥ 500 steps per day (OR = 0.21, 95% CI 0.05-0.88), compared to men. For every year of increase in age, the odds of increasing ≥ 500 steps per day decreased by 13% (OR = 0.87, 95% CI 0.78-0.97). Also, for every step increase in baseline self-efficacy, measured with the Self-Efficacy for Exercise Scale, the odds of increasing ≥ 500 steps per day increased by 14% (OR = 1.14, 95% CI 1.02-1.27). CONCLUSIONS: In the Sophia Step Study pedometer intervention, participants with a lower number of steps at baseline, male gender, lower age or higher baseline self-efficacy were more likely to respond to the intervention with a step increase above 500 steps per day. More knowledge is needed about factors that influence response to pedometer interventions. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02374788.

Associations of between- and within-day patterns of physical activity accumulation with arterial stiffness and indices of microvascular health-Evidence from The Maastricht study.

While physical activity (PA) is understood to promote vascular health, little is known about whether the daily and weekly patterns of PA accumulation associate with vascular health. Accelerometer-derived (activPAL3) 6- or 7-day stepping was analyzed for 6430 participants in The Maastricht Study (50.4% women; 22.4% Type 2 diabetes mellitus (T2DM)). Multivariable regression models examined associations between stepping metrics (average step count, and time spent slower and faster paced stepping) with arterial stiffness (measured as carotid-femoral pulse wave velocity (cfPWV)), and several indices of microvascular health (heat-induced skin hyperemia, retinal vessel reactivity and diameter), adjusting for confounders and moderators. PA pattern metrics were added to the regression models to identify associations with vascular health beyond that of stepping metrics. Analyses were stratified by T2DM status if an interaction effect was present. Average step count and time spent faster paced stepping was associated with better vascular health, and the association was stronger in those with compared to those without T2DM. In fully adjusted models a higher step count inter-daily stability was associated with a higher (worse) cfPWV in those without T2DM (std ß = 0.04, p = 0.007) and retinal venular diameter in the whole cohort (std ß = 0.07, p = 0.002). A higher within-day variability in faster paced stepping was associated with a lower (worse) heat-induced skin hyperemia in those with T2DM (std ß = -0.31, p = 0.008). Above and beyond PA volume, the daily and weekly patterns in which PA was accumulated were additionally associated with improved macro- and microvascular health, which may have implications for the prevention of vascular disease.

Pre-school-based behaviour change intervention to increase physical activity levels amongst young children: a feasibility cluster randomised controlled trial.

Background: A significant rise in childhood obesity worldwide over the past three decades highlights the urgent need for early interventions, especially in preschools as key settings for child development. This study aimed to assess the feasibility and fidelity of a randomised controlled trial of "I'm an Active Hero" (IAAH), a theory- and evidence-based multi-component behaviour change intervention targeting physical activity and sedentary behaviour amongst preschool-aged children. Methods: Two preschools in Taif city, Saudi Arabia were randomly assigned to either the intervention (n = 3 classrooms) or the usual curriculum control group (n = 3 classrooms). The intervention ran for 10 weeks from February to April 2023 and consisted of teacher-led physical activity and sedentary behaviour sessions in preschools, with an additional interactive home component. Primary outcome measures included intervention fidelity, recruitment rates, attrition rates, and compliance with trial procedures. Secondary outcomes included body mass index (BMI), objectively measured physical activity, and sedentary time via the ActiGraph GT3X accelerometer. Outcomes were measured at baseline and at 10 weeks in both study arms. Results: The preschool intervention component had high fidelity (93.3%), but the home component fidelity was lower (74%). A cluster-level recruitment rate of 12% (13/112 centres) was attained, whilst the individual-level recruitment rate stood at 36% (52/143 children, mean age of 4.16 years; 23 girls). Attrition was 10%. Compliance varied with 90% for BMI, 71% for accelerometery, and 45% for questionnaires. The intervention group showed small decreases in BMI, slight increases in physical activity, and decreases in sedentary time at follow-up compared to the control group. Parents, facilitators, and assistant teachers considered the intervention to be feasible and beneficial. Conclusion: The IAAH intervention was feasible to implement in Saudi Arabian preschools. Facilitators showed high fidelity in delivering it. However, preliminary data did not demonstrate effectiveness. A more comprehensive evaluation across a broader population is warranted. The intervention could be revised to optimise recruitment, compliance, and fidelity of the home-based component. Successful elements from this pilot should be retained whilst adaptations to implementation are made to strengthen key areas.Clinical trial registration: ClinicalTrials.gov, NCT05754359.

Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.

Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05).

Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents.

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7-22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = -0.15; 95% LOA [-0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M.

Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations.

The effect of replacing sedentary behavior with different intensities of physical activity on depression and anxiety in Chinese university students: an isotemporal substitution model.

BACKGROUND: Previous research has suggested that engaging in regular physical activity (PA) can help to reduce symptoms of depression and anxiety in university students. However, there is a lack of evidence regarding the impact of reducing sedentary behavior (SB) and increasing light-intensity PA (LPA) on these symptoms. This study aims to address this gap by using isotemporal substitution (IS) models to explore how substituting SB with LPA or moderate-to-vigorous PA (MVPA) affects depression and anxiety symptoms among university students. METHODS: The study recruited 318 university students with a mean age of 21.13 years. Accelerometers were used to objectively measure the time spent on SB, LPA, and MVPA, while depression and anxiety symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Self-rating Anxiety Scale (SAS). IS models using multivariable linear regression were employed to estimate the associations between different behaviors and depression and anxiety symptoms when 30 min of one behavior was substituted with another. RESULTS: In the single-activity model, less SB (ß = 0.321, 95% CI: 0.089, 1.297) and more MVPA (ß = -0.142, 95% CI: -1.496, - 0.071) were found to be significantly and negatively associated with depression scores, while less SB (ß = 0.343, 95% CI: 0.057, 1.014), LPA (ß = 0.132, 95% CI: 0.049, 1.023), and more MVPA (ß = -0.077, 95% CI: -1.446, - 0.052) were significantly and negatively correlated with anxiety scores. The IS analysis revealed that substituting 30 min of SB with LPA (ß = -0.202, 95% CI: -1.371, - 0.146) or MVPA (ß = -0.308, 95% CI: -0.970, - 0.073) was associated with improvements in depressive symptoms. Substituting 30 min of SB with MVPA (ß = -0.147, 95% CI: -1.863, - 0.034) was associated with reduced anxiety symptoms. CONCLUSION: Replacing 30 min of SB with MVPA may alleviate depression and anxiety symptoms in university students. Further research is needed to explore the long-term effects of PA interventions on the mental health disorders of this population.

Sensory integration and segmental control of posture during pregnancy.

BACKGROUND: Approximately 25% of pregnant people fall, yet the underlying mechanisms of this increased fall-risk remain unclear. Prior studies examining pregnancy and balance have utilized center of pressure analyses and reported mixed results. The purpose of this study was to examine sensory and segmental contributions to postural control throughout pregnancy using accelerometer-based measures of sway. METHODS: Thirty pregnant people (first trimester: n = 10, second trimester: n = 10, third trimester: n = 10) and 10 healthy, nonpregnant control people stood quietly for one minute in four conditions: eyes open on a firm surface, eyes closed on a firm surface, eyes open on a foam pad, and eyes closed on foam. Postural sway was quantified using the root mean square accelerations in the anterior-posterior and medial-lateral directions from an inertial sensor at the lumbar region. Sensory sway ratios, segmental coherence and co-phase, were calculated to assess sensory contributions and segmental control, respectively. FINDINGS: Pregnant people did not display greater sway compared to healthy, nonpregnant controls. There were no group differences in vestibular, visual, or somatosensory sway ratios, and no significant differences in balance control strategies between pregnant and nonpregnant participants across sensory conditions. INTERPRETATION: The small effects observed here contrast prior studies and suggest larger, definitive studies are needed to assess the effect of pregnancy on postural control. This study serves as a preliminary exploration of pregnant sensory and segmental postural control and highlights the need for future to hone the role of balance in fall risk during pregnancy.

Accurate fall risk classification in elderly using one gait cycle data and machine learning.

BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. This challenge stems from individual variability and step-to-step fluctuations, making accurate classification difficult. METHODS: We recruited 44 participants, equally divided into high and low fall-risk groups. A smartphone secured on their second sacral spinous process recorded data during indoor walking. Features were extracted at each gait cycle from a 6-dimensional time series (tri-axial angular velocity and tri-axial acceleration) and classified using the gradient boosting decision tree algorithm. FINDINGS: Mean accuracy across five-fold cross-validation was 0.936. "Age" was the most influential individual feature, while features related to acceleration in the gait direction held the highest total relative importance when aggregated by axis (0.5365). INTERPRETATION: Combining acceleration, angular velocity data, and the gradient boosting decision tree algorithm enabled accurate fall risk classification in the elderly, previously challenging due to lack of discernible features. We reveal the first-ever identification of three-dimensional pelvic motion characteristics during single gait cycles in the high-risk group. This novel method, requiring only one gait cycle, is valuable for individuals with physical limitations hindering repetitive or long-distance walking or for use in spaces with limited walking areas. Additionally, utilizing readily available smartphones instead of dedicated equipment has potential to improve gait analysis accessibility.

Weekend physical activity profiles and their relationship with quality of life: The SOPHYA cohort of Swiss children and adolescents.

INTRODUCTION: Quality of life (QoL) is an important health indicator among children and adolescents. Evidence on the effect of physical activity (PA)-related behaviors on QoL among youth remains inconsistent. Conventional accelerometer-derived PA metrics and guidelines with a focus on whole weeks may not adequately characterize QoL relevant PA behavior. OBJECTIVE: This study aims to a) identify clusters of accelerometer-derived PA profiles during weekend days among children and adolescents living in Switzerland, b) assess their cross-sectional and predictive association with overall QoL and its dimensions, and c) investigate whether the associations of QoL with the newly identified clusters persist upon adjustment for the commonly used PA metrics moderate-to-vigorous physical activity (MVPA) and time spent in sedentary behavior (SB). METHODS: The population-based Swiss children's Objectively measured PHYsical Activity (SOPHYA) cohort among children and adolescents aged 6 to 16 years was initiated at baseline in 2013. PA and QoL information was obtained twice over a five-year follow-up period. The primary endpoint is the overall QoL score and its six dimension scores obtained by KINDL® questionnaire. The primary predictor is the cluster membership of accelerometer-derived weekend PA profile. Clusters were obtained by applying the k-medoid algorithm to the distance matrix of profiles obtained by pairwise alignments of PA time series using the Dynamic Time Warping (DTW) algorithm. Secondary predictors are accelerometer-derived conventional PA metrics MVPA and SB from two combined weekend days. Linear regression models were applied to assess a) the cross-sectional association between PA cluster membership and QoL at baseline and b) the predictive association between PA cluster membership at baseline and QoL at follow-up, adjusting for baseline QoL. RESULTS: The study sample for deriving PA profile clusters consisted of 51.4% girls and had an average age of 10.9 [SD 2.5] years). The elbow and silhouette methods indicated that weekend PA profiles are best classified in two or four clusters. The most differentiating characteristic for the two-clusters classification ("lower activity" and "high activity"), and the four-clusters classification ("inactive", "low activity", "medium activity", and "high activity"), respectively was the participant's mean counts per 15-seconds epoch. Participants assigned to high activity clusters were younger and more often male. Neither the clustered PA profiles nor MVPA or SB were cross-sectionally or predictively associated with overall QoL. The only association of a conventional PA metrics with QoL while adjusting for cluster membership was observed between MVPA during the weekend days and social well-being with a mean score difference of 2.4 (95%CI: 0.3 to 4.5; p = 0.025). CONCLUSION: The absence of strong associations of PA metrics for the weekend with QoL, except for the positive association between MVPA during the weekend days and social well-being, is in line with results from two randomized studies not showing efficacy of PA interventions on youth QoL. But because PA decreases with age, its promotion and relevance to QoL remain important research topics. Larger longitudinal study samples with more than two follow-up time points of children and adolescents are needed to derive new novel accelerometer-derived PA profiles and to associate them with QoL dimensions.

Sustainable effects of a motor skill programme on physical activity levels in 7-8 years old children, in the Eastern Cape Province of South Africa.

BACKGROUND: Deteriorating global physical activity (PA) levels among children warrants new and sustainable approaches to increase PA levels. This study aimed to determine the immediate and sustainable influences of a 9-week movement program on the PA levels in 7 to 8-year-old school children in the Raymond Mhlaba Municipality in the Eastern Cape Province of South Africa. METHODS: A randomized control trial including two groups (control group (CG) and intervention group (IG)), pre-post-retest (after six months of no intervention) design was used. Seventy school children, mean age 7.12 years (± 0.71) (n = 35 IG; n = 35 CG) participated in the study. A 9-week movement program was followed twice a week for 30 min during school hours. PA was measured for 7 consecutive days using a hip-mounted wGT3X-BT Actigraph accelerometer. The Test of Gross Motor Development-Third Edition (TGMD-3) was used to assess motor skills. Hierarchical Linear Modelling (HLM) was applied to analyze the data with time, sex, and group as predictors. Effect sizes were computed using Cohen's d-cut points to assess the practical significance of changes over time. Estimated regression coefficients were also computed to determine the strength of the relationship between moderate-to-vigorous physical activity (MVPA) and fundamental movement skills (FMS). RESULTS: Before the intervention, 60% of the IG met the 60 min of daily MVPA guideline, while light physical activity (LPA) per day was also higher than sedentary behavior (SB) in both groups. No immediate (p < 0.01) or sustainable (p < 0.01) increases in MVPA levels were found and no positive associations emerged between FMS and MVPA levels. CONCLUSIONS: This intervention had little to no effect on children's MVPA. More understanding of the activity behavior and interests of children is needed to improve their PA behavior through the content of movement programs. Strategies are also needed to communicate clear messages at a personalized but also parental level, focusing on enhancing health through regular PA, especially to promote PA in young children.

Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach.

BACKGROUND: Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms is poorly understood. In this exploratory study, we hypothesize that a particular temporal pattern of daily physical activity could be associated with depressive symptoms and might be a better marker than the total amount of physical activity. METHODS: To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-h temporal patterns of physical activity. We conducted a cross-sectional study on NHANES 2011-2012 data collected from the noninstitutionalized civilian resident population of the United States. The number of participants that had the whole set of physical activity data collected by the accelerometer is 6613. Among 6613 participants, 4242 participants had complete demography and Patient Health Questionnaire-9 (PHQ-9) questionnaire, a tool to quantify depressive symptoms. The association between activity-count behaviors and depressive symptoms was analyzed using multivariable logistic regression to adjust for confounding factors in sequential models. RESULTS: We identified four physical activity-count behaviors based on five physical activity-counting patterns classified by unsupervised machine learning. Regarding PHQ-9 scores, we found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group. CONCLUSIONS: Our results might contribute to monitoring and identifying individuals with latent depressive symptoms, emphasizing the importance of nuanced activity patterns and their probability of assessing depressive symptoms effectively.